North Carolina State University's Mohammad Farazmand and Konstantinos Mamis corrected a flaw in a popular pandemic model, improving the accuracy of its predictions.
The researchers tweaked a key parameter in a stochastic compartmental model which segmented populations into different compartments, based on disease status. Each compartment has an associated equation containing certain parameters; health officials predict disease spread by filling in the values for each parameter within the equations.
The model must incorporate a certain amount of noise to mirror the fluctuating contact rate parameter, but Farazmand and Mamis found the addition of random "white noise" caused the compartmental model of the COVID-19 Omicron variant to underpredict disease spread. They applied the Ornstein-Uhlenbeck process to the model to more accurately correlate the forecast to actual data.
From North Carolina State University
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